Field of Art
The present invention relates generally to interactive support and modification of user behavior patterns, and more particularly to interactive healthcare and a novel variation of a behavioral support agent that incentivizes individuals to interact with and actively participate in utilizing online resources to enhance their overall wellness, it adaptively modifies the type and frequency of data collection based upon users' changing personal circumstances.
Description of Related Art
As the Internet evolves, access to information, including health-related information, continues to increase exponentially. Individuals currently have instant online access, from a wide variety of sources, to personal and aggregate information relating to patient histories, test results, physical and mental fitness, nutrition, health supplements, and a vast array of health research, as well as methods and devices for measuring, diagnosing and treating various conditions or simply enhancing overall wellness.
Moreover, the advent of “smart” mobile devices and related technologies has provided individuals with access to such information on a continuous basis, as well as the ability to monitor health-related data (heart rate, blood pressure, etc.) and receive feedback covering virtually any period of time, including events such as a workout or a night's sleep.
However, this vast of array of information and device technology is far from integrated, and can be overwhelming to individuals. How does a layperson even identify relevant symptoms, much less sift through the vast array of available information “on demand” to obtain useful results? And how personalized are those results given the limited amount of data provided by a few search terms? Does a search engine know what medications patients are taking, or whether they recently stopped taking a particular medication, or took another substance or engaged in an activity that, in combination, might explain certain symptoms?
There is clearly a need for a more holistic approach, particularly in light of the state of the technology currently available. In essence, there is a need for a “behavioral support agent” (BSA) that can facilitate the collection of relevant data on a continual basis, modify the type and frequency of such data collection based on changing circumstances, integrate such data with pertinent aggregate information, and guide individuals toward behaviors that will enhance their overall wellness.
Various attempts to address one or more aspects of this problem have been implemented or proposed. For example, websites such as WebMD provide users with the ability to search an extensive database of health-related information. In addition to providing a searchable database, other features include a “symptom checker” (which provides potential diagnoses based upon users' answers to questions relating to symptoms) and a “medication tracker” (which enables users to maintain lists of their current medications, and provides information relating to drug interactions, side effects, and FDA warnings). As noted above, however, such “on demand” systems provide limited personalization, as the information they provide is based on keyword searches rather than personal health-related data adaptively collected on a continual basis over time.
Other websites and smartphone apps are targeted at specific aspects of healthcare, such as maintaining personal medical records or medication profiles (e.g., HealthVault or MphRx) or monitoring activity and fitness levels, often in connection with user monitoring devices (e.g., FitBit). While some of these approaches may be more engaging than searchable websites, they do not take a holistic approach. Beyond specific activities such as walking, running or taking a pill, they lack comprehensive day-to-day and historical monitoring of users' symptoms, schedules and overall health conditions and the ability to adaptively collect, utilize and respond to such data to support users and enable them to improve their health outcomes.
Other systems have adopted a more holistic approach to this problem. For example, U.S. Pat. No. 8,170,609 discloses a personal virtual assistant system that includes a remote station carried by users which has one or more physiological sensors, and a rules engine that provides advice to users based in part upon the sensor data. Other systems have attempted to reduce the need for human intervention in routine aspects of healthcare by programming virtual assistants to aid users in various tasks. For instance, the “Alme” virtual assistant platform from NextIT provides automated aid with navigation of choices for users. It has been recently deployed by Aetna to help members navigate their website better. Ann, Alme's virtual assistant deployed by Aetna, is able to help the insured register on the website, get cost of service estimates, locate in-network providers and compare costs by facilities and physicians. Implementing Ann has enabled Aetna to cut costs by reducing the burden on their call center.
While some of these proposed “solutions” take a more patient-centric and preventive approach to wellness, some key obstacles remain unaddressed, even by existing virtual assistant systems. For example, healthcare is an expensive proposition. Regardless of the quality of the information obtained online, individuals may still find it necessary or desirable from time to time to visit clinics, hospitals, doctors and other specialists, and purchase medications, supplements, health monitoring devices and other health-related goods and services. The cost of such health-related goods and services can be quite significant, despite preventive measures. Additional cost-reduction efforts are still needed to address this problem effectively.
Moreover, as is the case with healthcare generally, the quality of information generated by any of these systems depends greatly upon the degree to which individuals participate in the process and interact with the system. For example, individuals will derive greater benefits if they provide timely and accurate information, follow system suggestions and provide frequent feedback regarding their activities, likes, dislikes and so forth. While users may initially provide profile information and frequently interact with a new system, human behavior is such that, in most cases, their level of interaction quickly tapers off.
While a BSA system could prompt users for relevant information on a timely basis and provide entertaining content in an effort to keep users engaged, additional incentives are needed to maintain sufficient patient participation in the process. Though seemingly unrelated, these problems of high healthcare costs and inadequate patient participation provide an opportunity for a novel approach that “kills two birds with one stone.”
A key deficiency of existing systems is their lack of connection to the “healthcare transaction flow” through which users purchase health-related goods and services. If a BSA system could insert itself into this transaction flow, and provide users with economic incentives (credit, discounts, etc.) based upon the nature and extent of their interaction with the system, such additional economic incentives would complete a feedback loop that reduces healthcare costs as a means of encouraging active user participation, which in turn enhances overall wellness.
Not surprisingly, some financial services companies have delved into the healthcare sector. For example, Citigroup's “Money2 for Health” project, is a payment processing and reconciliation system (online automated “spreadsheet” with integrated payment transaction capability) that allows consumers to maintain payment history and make payments from one portal to all healthcare providers and insurance companies registered on the site. But, this project neither includes nor suggests any connection to a BSA system, much less any reliance on patients' interaction with such a system as a factor in assessing benefits provided to patients, such as credit and discounts on the purchase of health-related goods and services.
While financial services companies such as Klarna (a European mobile payment provider based in Sweden), have experimented with “micro credit” and various other credit-assessment techniques (see, e.g., published patent applications WO/2013131971 and US/2011030738), no such company has even suggested targeting credit-assessment techniques at purchases of health-related goods and services, much less basing credit assessments on users' interaction with a BSA system.
Moreover, none of these existing systems employs a personalized approach to data collection, e.g., by adaptively modifying the type and frequency of data collection based upon users' changing personal circumstances.
Thus, there remains a need for a BSA system that addresses the problems of high healthcare costs and inadequate patient participation by enabling users to purchase health-related goods and services via their user accounts, adaptively modifying the type and frequency of data collection in a personalized manner, and automatically and dynamically providing economic incentives to users based upon the nature and extent of their interaction with the system.